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🚀 Excited to release AIBuildAI — an AI agent that automatically builds AI models 🏆 #1 on OpenAI MLE-Bench 💻 GitHub: Building AI models still requires a lot of manual work — designing models, writing code, training them, tuning hyperparameters, and iterating on results. We developed AIBuildAI to automate...

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Pengtao Xie

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151,764 görüntüleme • 4 ay önce •via X (Twitter)

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I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):
15:38

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Data Science Agents): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, May 21st, I'm sharing one of my best AI Projects: AI Customer Segmentation Agent with Python Register here (570+ registered):

Matt Dancho (Business Science)

35,452 görüntüleme • 1 yıl önce

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):
15:38

I'm excited to introduce my AI Machine Learning Agent that built 32 ML models in 30 seconds. Today, I'll share with you how to automate building 100s of ML models with the AI ML Agent, which is available on GitHub. We'll create an ML Agent focusing on a Customer Churn Problem. I'll guide you through setting up the ML Agent, creating dozens of ML models, and loading the best model for production. This AI is a huge time-saver! Table of Contents: 00:00 Introduction to my AI Data Science Team 02:56 Setting Up AI Data Science Team 04:48 Running the ML Agent Code 07:12 Create (and Run) the AI Machine Learning Agent 09:53 Reviewing ML Model Summary 12:00 Saving and Loading Models 13:00 Next Steps + Project Roadmap + AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, January 29th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (1570+ registered):

Matt Dancho (Business Science)

242,084 görüntüleme • 1 yıl önce

Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.
1:54

Chinese AI Model DeepSeq: Despite U.S. sanctions restricting access to Nvidia AI chips, Chinese researchers developed DeepSeq, an AI model reportedly more powerful than OpenAI’s. Overcoming Hardware Limitations: With only a limited supply of Nvidia chips, Chinese engineers achieved groundbreaking efficiency optimizations, reducing training resource requirements to just 1/100th of what OpenAI, Microsoft, or Google typically use. Open-Sourcing the Innovation: In a bold move, the developers open-sourced their AI models, making both the code and methodology freely available to the public, significantly reducing the competitive edge of proprietary models like OpenAI’s. Impact on AI Industry: The release of high-quality free alternatives disrupts the market, potentially devaluing OpenAI’s models and forcing American AI companies to rethink their strategies. China’s Rapid AI Innovation: The success of DeepSeq underscores China’s growing self-sufficiency in AI and its ability to leapfrog hardware limitations through advanced software and algorithmic breakthroughs.

Johannes Maria

24,512 görüntüleme • 1 yıl önce

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
4:45

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 görüntüleme • 4 ay önce

Prior to release, we shared a version of Cohere North Mini Code with AI engineers and answered some questions. Here's a quick illustrated walkthrough of the model's architecture and training process. Small models fill an important niche. They: 1. run on more widely available hardware 2. handle tasks within a certain range of complexity 3. take on sub-tasks that larger models delegate
4:53

Prior to release, we shared a version of Cohere North Mini Code with AI engineers and answered some questions. Here's a quick illustrated walkthrough of the model's architecture and training process. Small models fill an important niche. They: 1. run on more widely available hardware 2. handle tasks within a certain range of complexity 3. take on sub-tasks that larger models delegate

Jay Alammar

88,841 görüntüleme • 1 ay önce

I recorded a video tutorial on building an AI Code Assistant. Going through the full stack - from data models to the front end. Using Postgres, pg_vector, LangChain, OpenAI, and NextJS.
39:26

I recorded a video tutorial on building an AI Code Assistant. Going through the full stack - from data models to the front end. Using Postgres, pg_vector, LangChain, OpenAI, and NextJS.

Gwen (Chen) Shapira

30,463 görüntüleme • 2 yıl önce

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:
0:31

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

NVIDIA AI

12,152 görüntüleme • 5 ay önce

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.
0:52

1/ Excited to launch an experiment today - introducing Window, a way to use your own AI models on the web - including local ones! It's a bet on a new kind of AI app emerging, one that shifts model authentication and management to the user.

Alex Atallah

389,301 görüntüleme • 3 yıl önce

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.
0:54

𝐃𝐞𝐬𝐭𝐫𝐚 𝐎𝐂𝐀𝐈 (𝐎𝐧𝐞-𝐂𝐥𝐢𝐜𝐤 𝐀𝐈) 𝐌𝐨𝐝𝐞𝐥𝐬 - $DSYNC 𝐄𝐚𝐫𝐥𝐲 𝐀𝐜𝐜𝐞𝐬𝐬 𝐏𝐫𝐞𝐯𝐢𝐞𝐰 We are training and building a wide range of AI models for the Destra OCAI platform. Today, we will be showcasing an early preview of one of the initial models we are working on—a bullish/bearish prediction model designed to 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐭𝐡𝐞 𝐦𝐚𝐫𝐤𝐞𝐭 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐨𝐤𝐞𝐧𝐬. This is just one of the initial models we are working on; as we progress, the models will become more complex and intricate. In this behind-the-scenes video, we demonstrate how the model predicts the future market trend of the BTC-USD pair with an 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝟔𝟓%. This is the barebones structure of the models we are training, which will soon be available to users with a single click through the Destra OCAI platform. The OCAI platform includes a wide array of Destra Exclusive AI models, specifically designed to simplify life for crypto audiences, that are envisioned and built from scratch at Destra Labs.

Destra Network

37,149 görüntüleme • 2 yıl önce

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 görüntüleme • 2 yıl önce

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.
0:26

Announcing the Open Model Series: a new series on open source AI models, with merve of Hugging Face. Hugging Face is the open model hub: it hosts model weights, datasets, evals, & demo apps, and maintains the open-source libraries (like transformers) used to run and train them. I think of it like GitHub, but for AI models. You can download open models and run them on your own machines. In this series we'll cover what open source and open weight models are, how they work, how to run them yourself, and why they're important. By the way - this video is edited entirely with Claude! I can make a tutorial on how I did it. If there's a question you want answered about open models, leave it in the comments. We will read them all. Episode 1 is out soon.

Harper Carroll

30,392 görüntüleme • 9 gün önce

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries
0:59

As Larry Ellison explains, there are two types of AI models: real-time models, also called AI on the edge, where you need ultra-low latency decisions (think autonomous driving or robotics), and models that don’t need an immediate response, like ChatGPT, where the system has time to reason. Real-time models require local compute inside the device, and these chips have nothing to do with the GPUs running in data centers. The AI-on-the-edge market is still largely untapped — and it’s going to be a multi-trillion-dollar opportunity. I believe $AMD will be one of the biggest beneficiaries

Daniel

151,698 görüntüleme • 8 ay önce

Greg Brockman on AI-designed chips and the future of compute >openai used their own models to design their chips, faster and probably better than human engineers >in the future everyone would have an agent that works 24/7 for them. Restriction: compute, at the moment
2:12

Greg Brockman on AI-designed chips and the future of compute >openai used their own models to design their chips, faster and probably better than human engineers >in the future everyone would have an agent that works 24/7 for them. Restriction: compute, at the moment

Chubby♨️

209,854 görüntüleme • 10 ay önce

Today we're releasing Rainmaker to retail: an AI agent that trades sports markets on Polymarket for you. The AI reads every matchup, sizes every position, and executes automatically. Its MLB models are 56-24 this season. We built this to end manual sports betting, and it's live right now. Invite only, through referral links from existing members. Powered by Polymarket
0:31

Today we're releasing Rainmaker to retail: an AI agent that trades sports markets on Polymarket for you. The AI reads every matchup, sizes every position, and executes automatically. Its MLB models are 56-24 this season. We built this to end manual sports betting, and it's live right now. Invite only, through referral links from existing members. Powered by Polymarket

Rainmaker

17,580 görüntüleme • 19 gün önce

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."
0:35

$WDC CEO says storage demand and requirement compound and grow whether AI runs on US frontier models or cheaper open-source ones. "Whether it's the traditional frontier models that you've seen in the U.S. that are premium-based, or some of the new open-source, more economical models, the underlying requirement is still that they're going to require a lot of data to support the training of these models." "These models are going to generate a lot more data that requires storage for us." "So we view it as very positive, because even as compute maybe drives greater efficiency in terms of compute and memory resources, the requirement for storage is just going to compound and grow." "So we view it very positively for demand going forward."

Fireside Alpha

11,921 görüntüleme • 7 gün önce

Been experimenting with Apple's new Core AI framework over the past few days Built a mobile app that runs local AI models directly on device, exploring Core AI model deployment (.aimodel), model specialization, caching, and inference across Apple Silicon Also spent some time integrating with Apple's Foundation Models framework and understanding how custom models can coexist with Apple's on-device AI stack Still early, but the combination of Core AI, Foundation Models, and the Apple Neural Engine feels like a pretty compelling foundation for local first AI applications Still in alpha and working towards a more stable version, but it's been a fun way to explore where Apple's on device AI ecosystem is headed !!
0:28

Been experimenting with Apple's new Core AI framework over the past few days Built a mobile app that runs local AI models directly on device, exploring Core AI model deployment (.aimodel), model specialization, caching, and inference across Apple Silicon Also spent some time integrating with Apple's Foundation Models framework and understanding how custom models can coexist with Apple's on-device AI stack Still early, but the combination of Core AI, Foundation Models, and the Apple Neural Engine feels like a pretty compelling foundation for local first AI applications Still in alpha and working towards a more stable version, but it's been a fun way to explore where Apple's on device AI ecosystem is headed !!

Sagar

12,828 görüntüleme • 1 ay önce

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move
3:58

Revolutionizing Move Programming with OpenLedger In this demo, we showcase how Move datasets contributed by data providers to OpenLedger’s datanets are used to fine-tune specialized models with LoRA fine-tuning. As seen in the video, we showcase an example on how builders can deploy a Move-specialized model that powers Co-pilot agents using our no-code model fine-tuning platform. This is the future of AI and Web3 innovation. Watch this space to see more specialised models and data feeds being built for next generation agents on top of OpenLedger #Move

OpenLedger

61,662 görüntüleme • 1 yıl önce

EQTY Lab designs transparent and accountable tools for Responsible AI. We're excited to announce our first tool on Hugging Face: the AI Lineage Explorer — a solution that quickly visualizes the provenance of your models. AI Integrity begins here. 🧵
0:36

EQTY Lab designs transparent and accountable tools for Responsible AI. We're excited to announce our first tool on Hugging Face: the AI Lineage Explorer — a solution that quickly visualizes the provenance of your models. AI Integrity begins here. 🧵

EQTY Lab

44,351 görüntüleme • 2 yıl önce

Hugging Face CEO Clément Delague says “AI is actually an opportunity to fix a lot of the cybersecurity problems” because his company used Nvidia’s version of a Chinese open model to defend itself against a rogue AI agent developed by OpenAI. “That's the beauty of…open models,” he says. “Once they are shared, wherever they come from – from China or from anywhere – American companies can modify them, remix them, host them, run them themselves.”
3:18

Hugging Face CEO Clément Delague says “AI is actually an opportunity to fix a lot of the cybersecurity problems” because his company used Nvidia’s version of a Chinese open model to defend itself against a rogue AI agent developed by OpenAI. “That's the beauty of…open models,” he says. “Once they are shared, wherever they come from – from China or from anywhere – American companies can modify them, remix them, host them, run them themselves.”

Face The Nation

19,331 görüntüleme • 10 gün önce